This invention relates to improvements in a demand estimation apparatus for estimating a demand which fluctuates depending upon time zones, such as the traffic volume of elevators in a building and the electric power load of a power station.
The traffic volume of elevators in a building, the electric power load of a power station, or the like (hereinbelow, termed "demand") fluctuate irregularly when closely observed within a period of one day, but present similar aspects for the same time zones when observed over several days. In, for example, an office building, elevator passengers on their way to their office floors crowd on the first floor during a short period of time in the time zone in which they attend offices in the morning. In the first half of the lunch hour, many passengers go from the office floors to a restaurant floor, while in the latter half thereof, many passengers go from the restaurant floor and the first floor to the office floors. In addition, many passengers go from the office floors to the first floor in the time zone in which they leave the offices in the evening. The volume of traffic in the up direction and in the down direction are nearly equal in the daytime time zones other than mentioned above, while the volume of traffic becomes very small throughout the nighttime.
In order to deal with the traffic in the building changing in this manner by means of a limited number of elevators, the elevators are usually operated under group supervision. One of the important roles of the group supervision of the elevators is to assign an appropriate elevator to each hall call registered. Various assignment systems for the hall calls have been proposed. By way of example, there has been considered a system wherein, when a hall call is registered anew, it is tentatively assigned to respective elevators, and the waiting times of all hall calls, the possibility of the full capacity of passengers, etc. are predicted to calculate service evaluation values for all the cases, from among which the appropriate elevator is selected. In order to execute such predictive calculations, traffic data peculiar to each respective building is required. For example, data on the number of passengers who get on and off the cage of each elevator at intermediate floors are required for predicting the possibility of the full capacity. When such traffic data which changes every moment is stored each time, an enormous memory capacity is necessitated, which is not practical. Usually the required memory size is reduced by dividing the operating period of time in one day into several time zones, wherein only the average traffic volumes of the respective time zones are stored. After the completion of the building, however, there is a possibility that traffic data will change in accordance with changes in personnel organization in the building, and hence, it is difficult to obtain good traffic data with which the demand can be predicted accurately. For this reason, there has been proposed a system wherein traffic conditions in the building are continuously detected so as to sequentially improve traffic data.
More specifically, the operating period of time in one day is divided into K time zones (hereinbelow, termed "sections"), and a time (hereinbelow, termed "boundary") by which a section k-1 and a section k are bounded is denoted by t.sub.k (k=2, 3, . . . , K). Times t.sub.1 and t.sub.K+1 are the starting time and end time of the elevator operation, respectively. The average traffic volume P.sub.k (l) of the section k on the l-th day is given by the following Equation (1): ##EQU1##
Here, X.sub.k.sup.u (l) is a column vector of F-1 dimensions (where F denotes the number of floors) the elements of which are the number of passengers to get on cages in the up direction at respective floors in the time zone k of the l-th day. Similarly, X.sub.k.sup.d (l), Y.sub.k.sup.u (l) and Y.sub.k.sup.d (l) are column vectors which indicate the number of passengers to get on the cages in the down direction, the number of passengers to get off the cages in the up direction and the number of passengers to get off the cages in the down direction, respectively. (Where the letters X and Y represents the number of people getting on and off the elevator, respectively, and u and d represent the upward and downward direction of the elevator, respectively.) The average traffic volume (hereinbelow, termed "average demand") P.sub.k (l) is measured by a passenger-number detector which utilizes load changes during the stoppage of the cages of the elevators and/or industrial television, ultrasonic wave, or the like.
First, it will be considered to sequentially correct the representative value of the average demand P.sub.k (l) of each time zone in a case where the boundary t.sub.k which is the demarcating time between adjoining time zones is fixed.
It is thought that the columns {P.sub.k (1), P.sub.k (2), . . . } of the average demands occurring daily will range in the vicinity of a certain representative value P.sub.k. Since the magnitude of the representative value P.sub.k is unknown, it needs to be estimated by any method. In this case, there is the possibility that the magnitude itself of the representative value P.sub.k will change. The representative value is therefore predicted by taking a linear weighted average given in Equations (2) and (3) below and attaching more importance to the average demand P.sub.k (l) measured latest, than to the other average demands P.sub.k (1), P.sub.k (2), . . . and P.sub.k (l-1). ##EQU2##
Here, P.sub.k is the representative value which has been predicted from the average demands P.sub.k (1), . . . and P.sub.k (l) measured till the l-th day, and P.sub.k (O) is an initial value which is set to a suitable value and is set in advance. .lambda..sub.i denotes the weight of the average demand P.sub.k (i) measured on the i-th day, and this weight changes depending upon a parameter a. More specifically, an increase in the value of the parameter a results in an estimation in which more importance is attached to the latest measured average demand P.sub.k (l) than to the other average demands P.sub.k (1), . . . and P.sub.k (l-1), and in which the predictive representative value P.sub.k (l) quickly follows up the change of the representative value P.sub.k. However, when the value of the parameter a is too large, it is feared that the predictive representative value will change too violently in a manner to be influenced by the random variations of daily data. Meanwhile, Equations (2) and (3) can be rewritten as follows: EQU P.sub.k (l-a) P.sub.k (l -1)=+a P.sub.k (l) (4) EQU P.sub.k (O)=P.sub.k (O) (5)
In accordance with the above Equation (4), there is the advantage that the weighted average of Equation (2) can be calculated without storing the observation values P.sub.k (i), (i=1, 2, . . . , l-1) of the average demands in the past.
However, granted that the foregoing representative value P.sub.k (k=2, 3, . . . , K) of the average demand of each time zone has been precisely estimated, the deviation thereof from the actual demand is feared to become large near the demarcating boundary t.sub.k (k=2, 3, . . . , K) when the boundary t.sub.k itself is inappropriate. This large deviation brings about the disadvantage that the predictive calculations of the waiting times, the possibility of the full capacity, etc. become erroneous, so the elevators are not group-supervised as intended.